Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations49281
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 MiB
Average record size in memory120.0 B

Variable types

Numeric8
Categorical6

Alerts

sales_channel is highly imbalanced (50.4%) Imbalance
trip_type is highly imbalanced (94.3%) Imbalance
flight_hour has 1501 (3.0%) zeros Zeros

Reproduction

Analysis started2025-08-07 04:57:22.366574
Analysis finished2025-08-07 04:57:25.847392
Duration3.48 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

num_passengers
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5901869
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size770.0 KiB
2025-08-07T10:27:25.876943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0165375
Coefficient of variation (CV)0.63925663
Kurtosis10.076058
Mean1.5901869
Median Absolute Deviation (MAD)0
Skewness2.6880951
Sum78366
Variance1.0333485
MonotonicityNot monotonic
2025-08-07T10:27:25.919983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 30879
62.7%
2 12669
25.7%
3 2882
 
5.8%
4 1767
 
3.6%
5 544
 
1.1%
6 281
 
0.6%
7 107
 
0.2%
8 88
 
0.2%
9 64
 
0.1%
ValueCountFrequency (%)
1 30879
62.7%
2 12669
25.7%
3 2882
 
5.8%
4 1767
 
3.6%
5 544
 
1.1%
6 281
 
0.6%
7 107
 
0.2%
8 88
 
0.2%
9 64
 
0.1%
ValueCountFrequency (%)
9 64
 
0.1%
8 88
 
0.2%
7 107
 
0.2%
6 281
 
0.6%
5 544
 
1.1%
4 1767
 
3.6%
3 2882
 
5.8%
2 12669
25.7%
1 30879
62.7%

sales_channel
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size770.0 KiB
0
43917 
1
5364 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49281
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 43917
89.1%
1 5364
 
10.9%

Length

2025-08-07T10:27:25.967791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-07T10:27:26.006299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 43917
89.1%
1 5364
 
10.9%

Most occurring characters

ValueCountFrequency (%)
0 43917
89.1%
1 5364
 
10.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49281
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 43917
89.1%
1 5364
 
10.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49281
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 43917
89.1%
1 5364
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49281
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 43917
89.1%
1 5364
 
10.9%

trip_type
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size770.0 KiB
2
48779 
1
 
386
0
 
116

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49281
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 48779
99.0%
1 386
 
0.8%
0 116
 
0.2%

Length

2025-08-07T10:27:26.045673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-07T10:27:26.081445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 48779
99.0%
1 386
 
0.8%
0 116
 
0.2%

Most occurring characters

ValueCountFrequency (%)
2 48779
99.0%
1 386
 
0.8%
0 116
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49281
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 48779
99.0%
1 386
 
0.8%
0 116
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49281
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 48779
99.0%
1 386
 
0.8%
0 116
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49281
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 48779
99.0%
1 386
 
0.8%
0 116
 
0.2%

purchase_lead
Real number (ℝ)

Distinct470
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.723281
Minimum0
Maximum867
Zeros365
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size770.0 KiB
2025-08-07T10:27:26.126520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q121
median51
Q3115
95-th percentile287
Maximum867
Range867
Interquartile range (IQR)94

Descriptive statistics

Standard deviation90.410229
Coefficient of variation (CV)1.0671238
Kurtosis2.501577
Mean84.723281
Median Absolute Deviation (MAD)37
Skewness1.6568168
Sum4175248
Variance8174.0095
MonotonicityNot monotonic
2025-08-07T10:27:26.179667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 681
 
1.4%
2 666
 
1.4%
6 649
 
1.3%
7 636
 
1.3%
5 611
 
1.2%
13 604
 
1.2%
4 597
 
1.2%
8 591
 
1.2%
9 587
 
1.2%
12 582
 
1.2%
Other values (460) 43077
87.4%
ValueCountFrequency (%)
0 365
0.7%
1 681
1.4%
2 666
1.4%
3 571
1.2%
4 597
1.2%
5 611
1.2%
6 649
1.3%
7 636
1.3%
8 591
1.2%
9 587
1.2%
ValueCountFrequency (%)
867 1
< 0.1%
704 1
< 0.1%
641 1
< 0.1%
633 1
< 0.1%
625 1
< 0.1%
614 1
< 0.1%
606 1
< 0.1%
605 1
< 0.1%
584 1
< 0.1%
577 1
< 0.1%

length_of_stay
Real number (ℝ)

Distinct335
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.053976
Minimum0
Maximum778
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size770.0 KiB
2025-08-07T10:27:26.233016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q15
median17
Q328
95-th percentile84
Maximum778
Range778
Interquartile range (IQR)23

Descriptive statistics

Standard deviation33.832149
Coefficient of variation (CV)1.4675191
Kurtosis49.365201
Mean23.053976
Median Absolute Deviation (MAD)12
Skewness5.2964762
Sum1136123
Variance1144.6143
MonotonicityNot monotonic
2025-08-07T10:27:26.291435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 7611
15.4%
5 7151
14.5%
4 5568
 
11.3%
3 2798
 
5.7%
17 1823
 
3.7%
21 1373
 
2.8%
18 1335
 
2.7%
20 1277
 
2.6%
22 1272
 
2.6%
19 1241
 
2.5%
Other values (325) 17832
36.2%
ValueCountFrequency (%)
0 9
 
< 0.1%
1 257
 
0.5%
2 857
 
1.7%
3 2798
 
5.7%
4 5568
11.3%
5 7151
14.5%
6 7611
15.4%
17 1823
 
3.7%
18 1335
 
2.7%
19 1241
 
2.5%
ValueCountFrequency (%)
778 1
< 0.1%
773 1
< 0.1%
610 1
< 0.1%
603 1
< 0.1%
577 1
< 0.1%
573 1
< 0.1%
532 1
< 0.1%
517 1
< 0.1%
513 1
< 0.1%
510 1
< 0.1%

flight_hour
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0706763
Minimum0
Maximum23
Zeros1501
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size770.0 KiB
2025-08-07T10:27:26.341768image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median9
Q313
95-th percentile19
Maximum23
Range23
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.413099
Coefficient of variation (CV)0.59676906
Kurtosis-0.2998581
Mean9.0706763
Median Absolute Deviation (MAD)4
Skewness0.39814112
Sum447012
Variance29.30164
MonotonicityNot monotonic
2025-08-07T10:27:26.387071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
8 3125
 
6.3%
12 3114
 
6.3%
9 3097
 
6.3%
7 3080
 
6.2%
11 3071
 
6.2%
10 3050
 
6.2%
13 3046
 
6.2%
6 3010
 
6.1%
5 2817
 
5.7%
4 2786
 
5.7%
Other values (14) 19085
38.7%
ValueCountFrequency (%)
0 1501
3.0%
1 2071
4.2%
2 2596
5.3%
3 2616
5.3%
4 2786
5.7%
5 2817
5.7%
6 3010
6.1%
7 3080
6.2%
8 3125
6.3%
9 3097
6.3%
ValueCountFrequency (%)
23 975
 
2.0%
22 573
 
1.2%
21 386
 
0.8%
20 275
 
0.6%
19 294
 
0.6%
18 430
 
0.9%
17 848
 
1.7%
16 1536
3.1%
15 2201
4.5%
14 2783
5.6%

flight_day
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8136199
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size770.0 KiB
2025-08-07T10:27:26.424650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9919129
Coefficient of variation (CV)0.52231554
Kurtosis-1.2033931
Mean3.8136199
Median Absolute Deviation (MAD)2
Skewness0.1344121
Sum187939
Variance3.9677171
MonotonicityNot monotonic
2025-08-07T10:27:26.462862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 7988
16.2%
3 7562
15.3%
2 7558
15.3%
4 7323
14.9%
5 6685
13.6%
7 6442
13.1%
6 5723
11.6%
ValueCountFrequency (%)
1 7988
16.2%
2 7558
15.3%
3 7562
15.3%
4 7323
14.9%
5 6685
13.6%
6 5723
11.6%
7 6442
13.1%
ValueCountFrequency (%)
7 6442
13.1%
6 5723
11.6%
5 6685
13.6%
4 7323
14.9%
3 7562
15.3%
2 7558
15.3%
1 7988
16.2%

route
Real number (ℝ)

Distinct799
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean392.31331
Minimum0
Maximum798
Zeros20
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size770.0 KiB
2025-08-07T10:27:26.512351image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q1203
median382
Q3611
95-th percentile736
Maximum798
Range798
Interquartile range (IQR)408

Descriptive statistics

Standard deviation227.27043
Coefficient of variation (CV)0.5793085
Kurtosis-1.0960119
Mean392.31331
Median Absolute Deviation (MAD)200
Skewness-0.0059546058
Sum19333592
Variance51651.849
MonotonicityNot monotonic
2025-08-07T10:27:26.570428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 2620
 
5.3%
717 912
 
1.9%
633 833
 
1.7%
436 793
 
1.6%
287 729
 
1.5%
438 683
 
1.4%
293 677
 
1.4%
300 658
 
1.3%
291 652
 
1.3%
639 637
 
1.3%
Other values (789) 40087
81.3%
ValueCountFrequency (%)
0 20
 
< 0.1%
1 1
 
< 0.1%
2 2
 
< 0.1%
3 69
 
0.1%
4 8
 
< 0.1%
5 7
 
< 0.1%
6 2620
5.3%
7 6
 
< 0.1%
8 5
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
798 1
 
< 0.1%
797 7
 
< 0.1%
796 2
 
< 0.1%
795 6
 
< 0.1%
794 10
 
< 0.1%
793 2
 
< 0.1%
792 1
 
< 0.1%
791 6
 
< 0.1%
790 25
0.1%
789 2
 
< 0.1%

booking_origin
Real number (ℝ)

Distinct104
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.249589
Minimum0
Maximum103
Zeros78
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size770.0 KiB
2025-08-07T10:27:26.628527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q14
median37
Q358
95-th percentile93
Maximum103
Range103
Interquartile range (IQR)54

Descriptive statistics

Standard deviation32.785767
Coefficient of variation (CV)0.85715343
Kurtosis-1.1951302
Mean38.249589
Median Absolute Deviation (MAD)33
Skewness0.42598891
Sum1884978
Variance1074.9065
MonotonicityNot monotonic
2025-08-07T10:27:26.685776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 17691
35.9%
51 7055
 
14.3%
85 4502
 
9.1%
43 3819
 
7.7%
17 3284
 
6.7%
37 2317
 
4.7%
91 2042
 
4.1%
93 1993
 
4.0%
36 1258
 
2.6%
61 1060
 
2.2%
Other values (94) 4260
 
8.6%
ValueCountFrequency (%)
0 78
 
0.2%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 6
 
< 0.1%
4 17691
35.9%
5 8
 
< 0.1%
6 5
 
< 0.1%
7 36
 
0.1%
8 1
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
103 386
0.8%
102 1
 
< 0.1%
101 453
0.9%
100 173
 
0.4%
99 42
 
0.1%
98 5
 
< 0.1%
97 9
 
< 0.1%
96 2
 
< 0.1%
95 1
 
< 0.1%
94 2
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size770.0 KiB
1
32931 
0
16350 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49281
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 32931
66.8%
0 16350
33.2%

Length

2025-08-07T10:27:26.734989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-07T10:27:26.770704image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 32931
66.8%
0 16350
33.2%

Most occurring characters

ValueCountFrequency (%)
1 32931
66.8%
0 16350
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49281
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 32931
66.8%
0 16350
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49281
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 32931
66.8%
0 16350
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49281
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 32931
66.8%
0 16350
33.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size770.0 KiB
0
34712 
1
14569 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49281
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 34712
70.4%
1 14569
29.6%

Length

2025-08-07T10:27:26.809048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-07T10:27:26.843993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 34712
70.4%
1 14569
29.6%

Most occurring characters

ValueCountFrequency (%)
0 34712
70.4%
1 14569
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49281
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34712
70.4%
1 14569
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49281
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34712
70.4%
1 14569
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49281
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34712
70.4%
1 14569
29.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size770.0 KiB
0
28256 
1
21025 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49281
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 28256
57.3%
1 21025
42.7%

Length

2025-08-07T10:27:26.883044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-07T10:27:26.917962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 28256
57.3%
1 21025
42.7%

Most occurring characters

ValueCountFrequency (%)
0 28256
57.3%
1 21025
42.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49281
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 28256
57.3%
1 21025
42.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49281
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 28256
57.3%
1 21025
42.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49281
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 28256
57.3%
1 21025
42.7%

flight_duration
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2799738
Minimum4.67
Maximum9.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size770.0 KiB
2025-08-07T10:27:26.954490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4.67
5-th percentile4.67
Q15.62
median7.57
Q38.83
95-th percentile8.83
Maximum9.5
Range4.83
Interquartile range (IQR)3.21

Descriptive statistics

Standard deviation1.4963899
Coefficient of variation (CV)0.2055488
Kurtosis-1.3725837
Mean7.2799738
Median Absolute Deviation (MAD)1.26
Skewness-0.36211026
Sum358764.39
Variance2.2391828
MonotonicityNot monotonic
2025-08-07T10:27:26.998959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
8.83 14339
29.1%
8.58 6892
14.0%
5.62 5464
 
11.1%
6.62 4658
 
9.5%
7 3314
 
6.7%
7.57 2785
 
5.7%
4.67 2699
 
5.5%
6.42 1708
 
3.5%
5.33 1506
 
3.1%
4.75 1246
 
2.5%
Other values (11) 4670
 
9.5%
ValueCountFrequency (%)
4.67 2699
5.5%
4.72 486
 
1.0%
4.75 1246
 
2.5%
4.83 145
 
0.3%
5 237
 
0.5%
5.07 501
 
1.0%
5.13 116
 
0.2%
5.33 1506
 
3.1%
5.52 664
 
1.3%
5.62 5464
11.1%
ValueCountFrequency (%)
9.5 36
 
0.1%
8.83 14339
29.1%
8.67 787
 
1.6%
8.58 6892
14.0%
8.15 299
 
0.6%
7.57 2785
 
5.7%
7.42 221
 
0.4%
7 3314
 
6.7%
6.62 4658
 
9.5%
6.42 1708
 
3.5%

booking_complete
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size770.0 KiB
0
41890 
1
7391 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49281
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 41890
85.0%
1 7391
 
15.0%

Length

2025-08-07T10:27:27.044617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-07T10:27:27.079573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 41890
85.0%
1 7391
 
15.0%

Most occurring characters

ValueCountFrequency (%)
0 41890
85.0%
1 7391
 
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49281
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 41890
85.0%
1 7391
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49281
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 41890
85.0%
1 7391
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49281
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 41890
85.0%
1 7391
 
15.0%

Interactions

2025-08-07T10:27:25.276010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:22.781469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:23.232885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:23.550640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:23.959966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.294070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.618979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.954765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:25.318470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:22.859923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:23.275475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:23.596939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.012338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.338141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.663628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.997717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:25.354505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:22.948415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:23.312421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:23.636941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.050302image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.376106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.703238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:25.036168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:25.397257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:23.017356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:23.354746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:23.683057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.095440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.420424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.747555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:25.079685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:25.433304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:23.065116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:23.392474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:23.724083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.132965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.458522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.787309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:25.117329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:25.472305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:23.106907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:23.432207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:23.768765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.175869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.499015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.829184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:25.156944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:25.513391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:23.151964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:23.474483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:23.814545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.218783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.542110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.873544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:25.200913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:25.551191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:23.192935image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:23.513710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:23.917180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.257823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.580872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:24.914070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-08-07T10:27:25.238903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-08-07T10:27:27.110575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
booking_completebooking_originflight_dayflight_durationflight_hourlength_of_staynum_passengerspurchase_leadroutesales_channeltrip_typewants_extra_baggagewants_in_flight_mealswants_preferred_seat
booking_complete1.0000.2590.0130.1480.0470.0140.0270.0250.1090.0390.0280.0680.0260.050
booking_origin0.2591.000-0.008-0.2540.055-0.2560.0740.029-0.0720.0890.0370.0810.1430.114
flight_day0.013-0.0081.0000.0190.0190.0050.0180.040-0.0160.0470.0020.0040.0000.017
flight_duration0.148-0.2540.0191.000-0.0250.234-0.0570.079-0.1540.0600.0330.1000.1670.121
flight_hour0.0470.0550.019-0.0251.000-0.0490.0250.043-0.0120.0320.0000.0260.0270.028
length_of_stay0.014-0.2560.0050.234-0.0491.000-0.1250.025-0.0060.0300.0000.1040.0650.032
num_passengers0.0270.0740.018-0.0570.025-0.1251.0000.256-0.0580.0100.0000.1330.0290.055
purchase_lead0.0250.0290.0400.0790.0430.0250.2561.000-0.0990.0160.0070.0520.0400.020
route0.109-0.072-0.016-0.154-0.012-0.006-0.058-0.0991.0000.0480.0210.0950.0940.044
sales_channel0.0390.0890.0470.0600.0320.0300.0100.0160.0481.0000.0210.0560.0260.029
trip_type0.0280.0370.0020.0330.0000.0000.0000.0070.0210.0211.0000.0130.0130.004
wants_extra_baggage0.0680.0810.0040.1000.0260.1040.1330.0520.0950.0560.0131.0000.2170.208
wants_in_flight_meals0.0260.1430.0000.1670.0270.0650.0290.0400.0940.0260.0130.2171.0000.315
wants_preferred_seat0.0500.1140.0170.1210.0280.0320.0550.0200.0440.0290.0040.2080.3151.000

Missing values

2025-08-07T10:27:25.663060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-07T10:27:25.768666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

num_passengerssales_channeltrip_typepurchase_leadlength_of_stayflight_hourflight_dayroutebooking_originwants_extra_baggagewants_preferred_seatwants_in_flight_mealsflight_durationbooking_complete
020226219760611005.520
110211220360610005.520
2202243221730361105.520
31029631460610015.520
420268221530361015.520
51023482040611015.520
630220133640611015.520
7202238191410361015.520
81028022410610015.520
9112378301270360005.520
num_passengerssales_channeltrip_typepurchase_leadlength_of_stayflight_hourflight_dayroutebooking_originwants_extra_baggagewants_preferred_seatwants_in_flight_mealsflight_durationbooking_complete
4999010212610672040005.620
49991102866172040105.620
4999210214612572041005.620
4999310219612772041005.620
499942022569772040005.620
499952022769672041015.620
4999610211164772040005.620
4999710224622672040015.620
4999810215611172041015.620
4999910219610472040105.620